where \(\alpha \) , \(\beta \in [0, 1]\)  control the VLM score weights for base/novel categories, and the background score comes directly from the detector i.e., \(s(\mathbf {r}_b)_0 = z(\mathbf {r}_b)_0\) . Compared to the ensemble system in [1]}, our design is simpler without a need for knowledge distillation or double Faster R-CNN heads. We show ablations of different score fusion designs in Appendix REF .
